Quantum enhanced space based crop yield prediction system for nigerian agriculture: integrating quantum computing with satellite remote sensing

Abstract

Nigerian agriculture employs over 70 percent of rural populations and contributes substantially to national gross domestic product yet confronts mounting challenges from climate variability, resource constraints, and technological inadequacies threatening food security. Current crop yield prediction methodologies demonstrate critical limitations in accuracy, spatial resolution, and processing speed that hamper effective agricultural decision making. Classical computational approaches achieve prediction accuracies rarely exceeding 70 to 80 percent while requiring extended processing times that often render forecasts obsolete for practical farming applications. This research introduces QSCYPS (Quantum Enhanced Space Based Crop Yield Prediction System), integrating quantum computing architectures with satellite remote sensing across Nigerian agro ecological zones. The system employs three quantum algorithms: Quantum Fourier Transform for spectral feature extraction achieving O((log n)²) computational complexity versus classical O(n log n), Quantum Support Vector Machine for crop stress classification reaching 89 percent accuracy in simulation, and Quantum Approximate Optimization Algorithm for parameter calibration of DSSAT crop growth models. Theoretical analysis projects 42 percent reduction in root mean square error from 0.78 tonnes per hectare (classical hybrid baseline) to 0.45 tonnes per hectare for maize predictions, with coefficient of determination improving from 0.86 to 0.94. Computational scaling analysis demonstrates sixteen fold speedup at one million pixel processing scale, with advantages growing at national coverage scales exceeding 280 million pixels. The framework integrates Sentinel 2 imagery at ten metre resolution, ERA5 reanalysis climate data, and ISRIC SoilGrids parameters through Bayesian ensemble combining quantum extracted features with physics based crop models. While projections remain simulation based pending access to actual quantum hardware (IBM 127 qubit Eagle or Google Cirq platforms), QSCYPS establishes theoretical architecture and algorithmic foundation for operational quantum enhanced agricultural intelligence. This represents first integration of quantum information processing with satellite agricultural monitoring, positioning Nigerian institutions at forefront of quantum age precision agriculture.